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Generating Narrowband Automotive Acoustic Models from Measured Data with Artificial Neural Networks and Bayesian Optimization of Hyperparameters.

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Title: Generating Narrowband Automotive Acoustic Models from Measured Data with Artificial Neural Networks and Bayesian Optimization of Hyperparameters.
Authors: Tsokaktsidis, Dimitrios Ernst1 (AUTHOR) dimitrios.tsokaktsidis@tum.de, Maeder, Marcus1 (AUTHOR) marcus.maeder@tum.de, von Wysocki, Timo2 (AUTHOR) timo.von_wysocki@mercedes-benz.com, Nau, Clemens3 (AUTHOR) clemens.nau@mercedes-benz.com, Marburg, Steffen1 (AUTHOR) steffen.marburg@tum.de
Source: Journal of Theoretical & Computational Acoustics. Jun2025, Vol. 33 Issue 2, p1-26. 26p.
Database: Mathematics Source
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  Data: Generating Narrowband Automotive Acoustic Models from Measured Data with Artificial Neural Networks and Bayesian Optimization of Hyperparameters.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Theoretical+%26+Computational+Acoustics%22">Journal of Theoretical & Computational Acoustics</searchLink>. Jun2025, Vol. 33 Issue 2, p1-26. 26p.
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        Value: 10.1142/S2591728525500069
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      – Code: eng
        Text: English
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        PageCount: 26
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      – TitleFull: Generating Narrowband Automotive Acoustic Models from Measured Data with Artificial Neural Networks and Bayesian Optimization of Hyperparameters.
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            NameFull: Tsokaktsidis, Dimitrios Ernst
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            NameFull: Maeder, Marcus
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            NameFull: von Wysocki, Timo
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            NameFull: Nau, Clemens
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            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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              Value: 33
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